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CASP
Critical Assessment of Structure Prediction (CASP), sometimes called Critical Assessment of Protein Structure Prediction, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence many view the experiment more as a "world championship" in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaFold
AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing template structures were available from proteins with partially similar sequences. AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020. It achieved a level of accuracy much higher than any other entry. It scored above 90 on CASP's global distance test (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete matc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Protein Structure Prediction
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its Protein secondary structure, secondary and Protein tertiary structure, tertiary structure from Protein primary structure, primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology and addresses Levinthal's paradox. Accurate structure prediction has important applications in medicine (for example, in drug design) and biotechnology (for example, in novel enzyme design). Starting in 1994, the performance of current methods is assessed biannually in the ''Critical Assessment of Structure Prediction'' (CASP) experiment. A continuous evaluation of protein structure prediction web servers is performed by the community project ''Continuous Automated Model EvaluatiOn'' (CAMEO3D). Protein structure a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Global Distance Test
The global distance test (GDT), also written as GDT_TS to represent "total score", is a measure of similarity between two protein structures with known amino acid correspondences (e.g. identical amino acid sequences) but different tertiary structures. It is most commonly used to compare the results of protein structure prediction to the experimentally determined structure as measured by X-ray crystallography, protein NMR, or, increasingly, cryoelectron microscopy. The GDT metric was developed by Adam Zemla at Lawrence Livermore National Laboratory and originally implemented in the Local-Global Alignment (LGA) program. It is intended as a more accurate measurement than the common root-mean-square deviation (RMSD) metric - which is sensitive to outlier regions created, for example, by poor modeling of individual loop regions in a structure that is otherwise reasonably accurate. The conventional GDT_TS score is computed over the alpha carbon atoms and is reported as a percentage, r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
HH-suite
The HH-suite is an open-source software package for sensitive protein sequence searching. It contains programs that can search for similar protein sequences in protein sequence databases. Sequence searches are a standard tool in modern biology with which the function of unknown proteins can be inferred from the functions of proteins with similar sequences. HHsearch and HHblits are two main programs in the package and the entry point to its search function, the latter being a faster iteration. HHpred is an online server for protein structure prediction that uses homology information from HH-suite. The HH-suite searches for sequences using hidden Markov models (HMMs). The name comes from the fact that it performs HMM-HMM alignments. Among the most popular methods for protein sequence matching, the programs have been cited more than 5000 times total according to Google Scholar. Background Proteins are central players in all of life's processes. Understanding them is central to un ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Homology Modeling
Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "''target''" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "''template''"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of a sequence alignment that maps residues in the query sequence to residues in the template sequence. It has been seen that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure. Evolutionarily related proteins have similar sequences and naturally occurring homologous proteins have similar protein structure. It has been shown that three-dimensional protein structure is evolutionarily more conserved than would be expected on the basis of s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
De Novo Protein Structure Prediction
In computational biology, ''de novo'' protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid primary sequence. The problem itself has occupied leading scientists for decades while still remaining unsolved. According to Science, the problem remains one of the top 125 outstanding issues in modern science. At present, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins within 1.5 angstroms over the entire structure. ''De novo'' methods, a term first coined by William DeGrado, tend to require vast computational resources, and have thus only been carried out for relatively small proteins. De novo protein structure modeling is distinguished from Template-based modeling (TBM) by the fact that no solved homologue to the protein of interest is used, making efforts to predict protein structure from amino acid sequence exceedingly difficult. Predi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Protein Structure Initiative
The Protein Structure Initiative (PSI) was a USA based project that aimed at accelerating discovery in structural genomics and contribute to understanding biological function. Funded by the U.S. National Institute of General Medical Sciences (NIGMS) between 2000 and 2015, its aim was to reduce the cost and time required to determine three-dimensional protein structures and to develop techniques for solving challenging problems in structural biology, including membrane proteins. Over a dozen research centers have been supported by the PSI for work in building and maintaining high-throughput structural genomics pipelines, developing computational protein structure prediction methods, organizing and disseminating information generated by the PSI, and applying high-throughput structure determination to study a broad range of important biological and biomedical problems. The project has been organized into three separate phases. The first phase of the Protein Structure Initiative (PSI- ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bioinformatics
Bioinformatics () is an interdisciplinary field of science that develops methods and Bioinformatics software, software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, data science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology, however this distinction between the two terms is often disputed. To some, the term ''computational biology'' refers to building and using models of biological systems. Computational, statistical, and computer programming techniques have been used for In silico, computer simulation analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of genomics, such as by the identification of genes and single nucleotide polymorphis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LiveBench
LiveBench is a continuously running benchmark project for assessing the quality of protein structure prediction and secondary structure prediction methods. LiveBench focuses mainly on homology modeling and protein threading but also includes secondary structure prediction, comparing publicly available webserver output to newly deposited protein structures in the Protein Data Bank. Like the EVA project and unlike the related CASP and CAFASP CAFASP, or the Critical Assessment of Fully Automated Structure Prediction, is a large-scale blind experiment in protein structure prediction that studies the performance of automated structure prediction webservers in homology modeling, fold rec ... experiments, LiveBench is intended to study the accuracy of predictions that would be obtained by non-expert users of publicly available prediction methods. A major advantage of LiveBench and EVA over CASP projects, which run once every two years, is their comparatively large data set. Referen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Critical Assessment Of Function Annotation
The Critical Assessment of Function Annotation (CAFA) is an ongoing community-driven experiment designed to evaluate computational methods for protein function prediction. Organized as a recurring challenge since 2010, CAFA aims to improve the accuracy, transparency, and benchmarking of algorithms that predict the biological function of proteins, using ontologies such as the Gene Ontology (GO). By fostering open and rigorous assessments, CAFA has become a central benchmark in computational biology and bioinformatics. Overview CAFA assesses methods by comparing predictions made by participating teams against experimentally determined annotations that accumulate over time in public protein databases. Predictions are submitted blindly before a predefined ''target accumulation period'', during which newly curated experimental data becomes available. This approach enables objective evaluation of methods without bias from known annotations. The goal is to assign current labels from the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DeepMind
DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is headquartered in London, with research centres in the United States, Canada, France, Germany, and Switzerland. DeepMind introduced neural Turing machines (neural networks that can access external memory like a conventional Turing machine), resulting in a computer that loosely resembles short-term memory in the human brain. DeepMind has created neural network models to play video games and board games. It made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, a world champion, in a five-game match, which was the subject of a documentary film. A more general program, AlphaZer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
